42 citations · 94 across the 13 of their papers we have counts for
Showing stat.MLShow all
3 papers · 1 filter
stat.ML2026
Nonlinear multi-study sparse factor analysis
Gemma E. Moran, Anandi Krishnan
High-dimensional data often exhibit variation that can be captured by lower-dimensional factors. For high-dimensional data from multiple studies, one goal is to understand which un…
stat.ML2025
Towards Interpretable Deep Generative Models via Causal Representation Learning
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art perform…
stat.ML2021★ 2 cited
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran, Dhanya Sridhar, Yixin Wang +1
We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors (representations) which summarize the ass…